{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch, torch.nn as nn, torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch.utils.data as data_utils\n",
    "import tensorboardX\n",
    "\n",
    "train = pd.read_csv('creditcard_train.csv')\n",
    "\n",
    "feats = [f for f in train.columns if f not in ['Class','class','Unnamed: 0','class_pred','Index']]\n",
    "X = train[feats].values\n",
    "y = train['Class'].values\n",
    "\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "class FraudNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(30, 16)\n",
    "        self.fc2 = nn.Linear(16, 18)\n",
    "        self.fc3 = nn.Linear(18, 20)\n",
    "        self.fc4 = nn.Linear(20, 24)\n",
    "        self.fc5 = nn.Linear(24, 1)\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.dropout(x, p=0.25)\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = F.relu(self.fc4(x))\n",
    "        x = F.sigmoid(self.fc5(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = FraudNet().double()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.from_numpy(X)\n",
    "Y_train = torch.from_numpy(y).double()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.BCELoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_epochs = 2\n",
    "minibatch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data_utils.TensorDataset(X_train, Y_train)\n",
    "train_loader = data_utils.DataLoader(train, batch_size=minibatch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51266"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51266"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = torch.from_numpy(X_test)\n",
    "Y_test = torch.from_numpy(Y_test).double()\n",
    "test = data_utils.TensorDataset(X_test, Y_test)\n",
    "test_loader = data_utils.DataLoader(test, batch_size=minibatch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wufei/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
      "/Users/wufei/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1594: UserWarning: Using a target size (torch.Size([64])) that is different to the input size (torch.Size([64, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     "text": [
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     "output_type": "stream",
     "text": [
      "/Users/wufei/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1594: UserWarning: Using a target size (torch.Size([7])) that is different to the input size (torch.Size([7, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epochs: 1, batch: 4005 loss: 0.7648404545964101\n",
      "12.629472017288208\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start = time.time()\n",
    "for i in range(training_epochs):\n",
    "    for b, data in enumerate(train_loader, 0):\n",
    "        inputs, labels = data\n",
    "        y_pred = net(inputs)\n",
    "        loss = loss_fn(y_pred, labels)\n",
    "        \n",
    "        if b % 100:\n",
    "            print('Epochs: {}, batch: {} loss: {}'.format(i, b, loss))\n",
    "        #reset gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # backward pass\n",
    "        loss.backward()\n",
    "\n",
    "        # update weights\n",
    "        optimizer.step()\n",
    "end = time.time()\n",
    "\n",
    "print(end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wufei/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 51266 inputs: 99.84004993562985\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data in test_loader:\n",
    "        inputs, labels = data\n",
    "        outputs = net(inputs)\n",
    "        average_precision_score(labels,outputs)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted.double() == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the {} inputs: {}'.format(\n",
    "    X_test.shape[0], 100 * correct/total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wufei/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    }
   ],
   "source": [
    "outputs = net(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "average_precision = average_precision_score(Y_test,outputs.detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d8a18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    precision, recall, _ = precision_recall_curve(Y_test,outputs.detach().numpy())\n",
    "    plt.step(recall, precision, color='r', alpha=0.2,where='post')\n",
    "    plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                     color='#f25269')\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.title('Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.3f}'.format(average_precision),fontsize=16)\n",
    "    plt.savefig('NN_PR.png',dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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